M-estimators for Isotonic Regression
- Autores
- Alvarez, Enrique Ernesto; Yohai, Victor Jaime
- Año de publicación
- 2012
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In this paper we propose a family of robust estimates for isotonic regression: isotonic M-estimators. We show that their asymptotic distribution is, up to an scalar factor, the same as that of Brunk’s classical isotonic estimator. We also derive the influence function and the breakdown point of these estimates. Finally we perform a Monte Carlo study that shows that the proposed family includes estimators that are simultaneously highly efficient under Gaussian errors and highly robust when the error distribution has heavy tails.
Fil: Alvarez, Enrique Ernesto. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina - Materia
-
ISOTONIC REGRESSION
M-ESTIMATORS
ROBUST ESTIMATES - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/198372
Ver los metadatos del registro completo
id |
CONICETDig_ef367f915e0ab7c4db837e86a0704f43 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/198372 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
spelling |
M-estimators for Isotonic RegressionAlvarez, Enrique ErnestoYohai, Victor JaimeISOTONIC REGRESSIONM-ESTIMATORSROBUST ESTIMATEShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper we propose a family of robust estimates for isotonic regression: isotonic M-estimators. We show that their asymptotic distribution is, up to an scalar factor, the same as that of Brunk’s classical isotonic estimator. We also derive the influence function and the breakdown point of these estimates. Finally we perform a Monte Carlo study that shows that the proposed family includes estimators that are simultaneously highly efficient under Gaussian errors and highly robust when the error distribution has heavy tails.Fil: Alvarez, Enrique Ernesto. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaElsevier Science2012-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/198372Alvarez, Enrique Ernesto; Yohai, Victor Jaime; M-estimators for Isotonic Regression; Elsevier Science; Journal Of Statistical Planning And Inference; 142; 8; 8-2012; 2351-23680378-3758CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378375812000985info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jspi.2012.02.051info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:27Zoai:ri.conicet.gov.ar:11336/198372instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:03:27.863CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
M-estimators for Isotonic Regression |
title |
M-estimators for Isotonic Regression |
spellingShingle |
M-estimators for Isotonic Regression Alvarez, Enrique Ernesto ISOTONIC REGRESSION M-ESTIMATORS ROBUST ESTIMATES |
title_short |
M-estimators for Isotonic Regression |
title_full |
M-estimators for Isotonic Regression |
title_fullStr |
M-estimators for Isotonic Regression |
title_full_unstemmed |
M-estimators for Isotonic Regression |
title_sort |
M-estimators for Isotonic Regression |
dc.creator.none.fl_str_mv |
Alvarez, Enrique Ernesto Yohai, Victor Jaime |
author |
Alvarez, Enrique Ernesto |
author_facet |
Alvarez, Enrique Ernesto Yohai, Victor Jaime |
author_role |
author |
author2 |
Yohai, Victor Jaime |
author2_role |
author |
dc.subject.none.fl_str_mv |
ISOTONIC REGRESSION M-ESTIMATORS ROBUST ESTIMATES |
topic |
ISOTONIC REGRESSION M-ESTIMATORS ROBUST ESTIMATES |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
In this paper we propose a family of robust estimates for isotonic regression: isotonic M-estimators. We show that their asymptotic distribution is, up to an scalar factor, the same as that of Brunk’s classical isotonic estimator. We also derive the influence function and the breakdown point of these estimates. Finally we perform a Monte Carlo study that shows that the proposed family includes estimators that are simultaneously highly efficient under Gaussian errors and highly robust when the error distribution has heavy tails. Fil: Alvarez, Enrique Ernesto. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina |
description |
In this paper we propose a family of robust estimates for isotonic regression: isotonic M-estimators. We show that their asymptotic distribution is, up to an scalar factor, the same as that of Brunk’s classical isotonic estimator. We also derive the influence function and the breakdown point of these estimates. Finally we perform a Monte Carlo study that shows that the proposed family includes estimators that are simultaneously highly efficient under Gaussian errors and highly robust when the error distribution has heavy tails. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-08 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/198372 Alvarez, Enrique Ernesto; Yohai, Victor Jaime; M-estimators for Isotonic Regression; Elsevier Science; Journal Of Statistical Planning And Inference; 142; 8; 8-2012; 2351-2368 0378-3758 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/198372 |
identifier_str_mv |
Alvarez, Enrique Ernesto; Yohai, Victor Jaime; M-estimators for Isotonic Regression; Elsevier Science; Journal Of Statistical Planning And Inference; 142; 8; 8-2012; 2351-2368 0378-3758 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378375812000985 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jspi.2012.02.051 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
_version_ |
1842269801377955840 |
score |
13.13397 |